TOURIST SPENDING PREDICTION MODEL

CONTEXT: The Tanzanian tourism sector plays a significant role in the Tanzanian economy, contributing about 17% to the country’s GDP and 25% of all foreign exchange revenues. The sector, which provides direct employment for more than 600,000 people and up to 2 million people indirectly, generated approximately $2.4 billion in 2018 according to government statistics. Tanzania received a record 1.1 million international visitor arrivals in 2014, mostly from Europe, the US and Africa. Tanzania is the only country in the world which has allocated more than 25% of its total area for wildlife, national parks, and protected areas.There are 16 national parks in Tanzania, 28 game reserves, 44 game-controlled areas, two marine parks and one conservation area.

AIM: The aim of this project is to explore and build a linear regression model that will predict the spending behaivior of tourists visiting Tanzania.The model can be used by different tour operators and the Tanzania Tourism Board to automatically help tourists across the world estimate their expenditure before visiting Tanzania.

#IMPORTING NECESSARY LIBRARIES
import pandas as pd
import numpy as np

import seaborn as sns
import matplotlib.pyplot as plt

%matplotlib inline

#Importing dataset for the analysis.
Tz = pd.read_csv("Train .csv")
Tz

	ID	country	age_group	travel_with	total_female	total_male	purpose	main_activity	info_source	tour_arrangement	...	package_transport_tz	package_sightseeing	package_guided_tour	package_insurance	night_mainland	night_zanzibar	payment_mode	first_trip_tz	most_impressing	total_cost
0	tour_0	SWIZERLAND	45-64	Friends/Relatives	1.0	1.0	Leisure and Holidays	Wildlife tourism	Friends, relatives	Independent	...	No	No	No	No	13.0	0.0	Cash	No	Friendly People	674602.5
1	tour_10	UNITED KINGDOM	25-44	NaN	1.0	0.0	Leisure and Holidays	Cultural tourism	others	Independent	...	No	No	No	No	14.0	7.0	Cash	Yes	Wonderful Country, Landscape, Nature	3214906.5
2	tour_1000	UNITED KINGDOM	25-44	Alone	0.0	1.0	Visiting Friends and Relatives	Cultural tourism	Friends, relatives	Independent	...	No	No	No	No	1.0	31.0	Cash	No	Excellent Experience	3315000.0
3	tour_1002	UNITED KINGDOM	25-44	Spouse	1.0	1.0	Leisure and Holidays	Wildlife tourism	Travel, agent, tour operator	Package Tour	...	Yes	Yes	Yes	No	11.0	0.0	Cash	Yes	Friendly People	7790250.0
4	tour_1004	CHINA	1-24	NaN	1.0	0.0	Leisure and Holidays	Wildlife tourism	Travel, agent, tour operator	Independent	...	No	No	No	No	7.0	4.0	Cash	Yes	No comments	1657500.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
4804	tour_993	UAE	45-64	Alone	0.0	1.0	Business	Hunting tourism	Friends, relatives	Independent	...	No	No	No	No	2.0	0.0	Credit Card	No	No comments	3315000.0
4805	tour_994	UNITED STATES OF AMERICA	25-44	Spouse	1.0	1.0	Leisure and Holidays	Wildlife tourism	Travel, agent, tour operator	Package Tour	...	Yes	Yes	Yes	Yes	11.0	0.0	Cash	Yes	Friendly People	10690875.0
4806	tour_995	NETHERLANDS	1-24	NaN	1.0	0.0	Leisure and Holidays	Wildlife tourism	others	Independent	...	No	No	No	No	3.0	7.0	Cash	Yes	Good service	2246636.7
4807	tour_997	SOUTH AFRICA	25-44	Friends/Relatives	1.0	1.0	Business	Beach tourism	Travel, agent, tour operator	Independent	...	No	No	No	No	5.0	0.0	Credit Card	No	Friendly People	1160250.0
4808	tour_999	UNITED KINGDOM	25-44	Spouse	1.0	1.0	Leisure and Holidays	Wildlife tourism	Travel, agent, tour operator	Package Tour	...	Yes	Yes	Yes	No	4.0	7.0	Cash	Yes	Friendly People	13260000.0

4809 rows × 23 columns

#Showing the top most records
Tz.head(20)

	ID	country	age_group	travel_with	total_female	total_male	purpose	main_activity	info_source	tour_arrangement	...	package_transport_tz	package_sightseeing	package_guided_tour	package_insurance	night_mainland	night_zanzibar	payment_mode	first_trip_tz	most_impressing	total_cost
0	tour_0	SWIZERLAND	45-64	Friends/Relatives	1.0	1.0	Leisure and Holidays	Wildlife tourism	Friends, relatives	Independent	...	No	No	No	No	13.0	0.0	Cash	No	Friendly People	674602.5
1	tour_10	UNITED KINGDOM	25-44	NaN	1.0	0.0	Leisure and Holidays	Cultural tourism	others	Independent	...	No	No	No	No	14.0	7.0	Cash	Yes	Wonderful Country, Landscape, Nature	3214906.5
2	tour_1000	UNITED KINGDOM	25-44	Alone	0.0	1.0	Visiting Friends and Relatives	Cultural tourism	Friends, relatives	Independent	...	No	No	No	No	1.0	31.0	Cash	No	Excellent Experience	3315000.0
3	tour_1002	UNITED KINGDOM	25-44	Spouse	1.0	1.0	Leisure and Holidays	Wildlife tourism	Travel, agent, tour operator	Package Tour	...	Yes	Yes	Yes	No	11.0	0.0	Cash	Yes	Friendly People	7790250.0
4	tour_1004	CHINA	1-24	NaN	1.0	0.0	Leisure and Holidays	Wildlife tourism	Travel, agent, tour operator	Independent	...	No	No	No	No	7.0	4.0	Cash	Yes	No comments	1657500.0
5	tour_1005	UNITED KINGDOM	25-44	NaN	0.0	1.0	Leisure and Holidays	Wildlife tourism	Travel, agent, tour operator	Package Tour	...	No	Yes	Yes	No	9.0	3.0	Cash	Yes	Wildlife	120950.0
6	tour_1007	SOUTH AFRICA	45-64	Alone	0.0	1.0	Business	Mountain climbing	Friends, relatives	Independent	...	No	No	No	No	9.0	0.0	Cash	Yes	Friendly People	466140.0
7	tour_1008	UNITED STATES OF AMERICA	45-64	Friends/Relatives	1.0	1.0	Leisure and Holidays	Wildlife tourism	Travel, agent, tour operator	Package Tour	...	Yes	Yes	Yes	Yes	10.0	3.0	Cash	Yes	Friendly People	3480750.0
8	tour_101	NIGERIA	25-44	Alone	0.0	1.0	Leisure and Holidays	Cultural tourism	Travel, agent, tour operator	Independent	...	No	No	No	No	4.0	0.0	Cash	Yes	NaN	994500.0
9	tour_1011	INDIA	25-44	Alone	1.0	0.0	Business	Wildlife tourism	Travel, agent, tour operator	Independent	...	No	No	No	No	5.0	0.0	Credit Card	Yes	Friendly People	2486250.0
10	tour_1012	BRAZIL	25-44	Spouse	1.0	1.0	Leisure and Holidays	Wildlife tourism	Radio, TV, Web	Independent	...	No	No	No	No	17.0	3.0	Cash	Yes	Wonderful Country, Landscape, Nature	1117155.0
11	tour_1013	CANADA	45-64	Children	2.0	0.0	Leisure and Holidays	Beach tourism	Friends, relatives	Independent	...	No	No	No	No	30.0	0.0	Cash	No	Excellent Experience	8121750.0
12	tour_1016	CANADA	45-64	Children	0.0	2.0	Leisure and Holidays	Wildlife tourism	Travel, agent, tour operator	Independent	...	No	No	No	No	11.0	3.0	Cash	Yes	No comments	331500.0
13	tour_1017	MALT	25-44	Friends/Relatives	2.0	0.0	Leisure and Holidays	Wildlife tourism	Friends, relatives	Package Tour	...	Yes	No	No	No	10.0	0.0	Cash	Yes	No comments	11346650.0
14	tour_1018	MOZAMBIQUE	25-44	Alone	0.0	1.0	Visiting Friends and Relatives	Beach tourism	Friends, relatives	Independent	...	No	No	No	No	2.0	0.0	Cash	Yes	Wildlife	497250.0
15	tour_102	RWANDA	65+	Alone	1.0	0.0	Leisure and Holidays	Beach tourism	Friends, relatives	Independent	...	No	No	No	No	0.0	2.0	Cash	Yes	Wonderful Country, Landscape, Nature	331500.0
16	tour_1021	AUSTRIA	45-64	Friends/Relatives	4.0	1.0	Visiting Friends and Relatives	Mountain climbing	Friends, relatives	Independent	...	No	No	No	No	24.0	0.0	Cash	No	Friendly People	2000000.0
17	tour_1022	MYANMAR	25-44	NaN	1.0	0.0	Meetings and Conference	Wildlife tourism	Radio, TV, Web	Independent	...	No	No	No	No	5.0	0.0	Cash	Yes	Friendly People	331500.0
18	tour_1024	GERMANY	25-44	Children	1.0	1.0	Visiting Friends and Relatives	Cultural tourism	Friends, relatives	Independent	...	No	No	No	No	3.0	0.0	Cash	Yes	Friendly People	2269330.0
19	tour_1026	KENYA	25-44	NaN	1.0	0.0	Business	Mountain climbing	Friends, relatives	Independent	...	No	No	No	No	4.0	0.0	Cash	No	Friendly People	377520.0

20 rows × 23 columns

#Information about the tabular data.
Tz.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 4809 entries, 0 to 4808
Data columns (total 23 columns):
 #   Column                 Non-Null Count  Dtype  
---  ------                 --------------  -----  
 0   ID                     4809 non-null   object 
 1   country                4809 non-null   object 
 2   age_group              4809 non-null   object 
 3   travel_with            3695 non-null   object 
 4   total_female           4806 non-null   float64
 5   total_male             4804 non-null   float64
 6   purpose                4809 non-null   object 
 7   main_activity          4809 non-null   object 
 8   info_source            4809 non-null   object 
 9   tour_arrangement       4809 non-null   object 
 10  package_transport_int  4809 non-null   object 
 11  package_accomodation   4809 non-null   object 
 12  package_food           4809 non-null   object 
 13  package_transport_tz   4809 non-null   object 
 14  package_sightseeing    4809 non-null   object 
 15  package_guided_tour    4809 non-null   object 
 16  package_insurance      4809 non-null   object 
 17  night_mainland         4809 non-null   float64
 18  night_zanzibar         4809 non-null   float64
 19  payment_mode           4809 non-null   object 
 20  first_trip_tz          4809 non-null   object 
 21  most_impressing        4496 non-null   object 
 22  total_cost             4809 non-null   float64
dtypes: float64(5), object(18)
memory usage: 864.2+ KB

#Statistical description of numerical features.
Tz.describe()

	total_female	total_male	night_mainland	night_zanzibar	total_cost
count	4806.000000	4804.000000	4809.000000	4809.000000	4.809000e+03
mean	0.926758	1.009575	8.488043	2.304429	8.114389e+06
std	1.288242	1.138865	10.427624	4.227080	1.222490e+07
min	0.000000	0.000000	0.000000	0.000000	4.900000e+04
25%	0.000000	1.000000	3.000000	0.000000	8.121750e+05
50%	1.000000	1.000000	6.000000	0.000000	3.397875e+06
75%	1.000000	1.000000	11.000000	4.000000	9.945000e+06
max	49.000000	44.000000	145.000000	61.000000	9.953288e+07

FINDING AND REPLACING MISSING/NULL VALUES.

Tz.isnull().sum()

ID                          0
country                     0
age_group                   0
travel_with              1114
total_female                3
total_male                  5
purpose                     0
main_activity               0
info_source                 0
tour_arrangement            0
package_transport_int       0
package_accomodation        0
package_food                0
package_transport_tz        0
package_sightseeing         0
package_guided_tour         0
package_insurance           0
night_mainland              0
night_zanzibar              0
payment_mode                0
first_trip_tz               0
most_impressing           313
total_cost                  0
dtype: int64

travel_with, total-female, total male and most_impressing columns have NaN/Null values which needs to be filled.

#Travel_with
Tz['travel_with'].fillna('Alone',inplace=True)
Tz

	ID	country	age_group	travel_with	total_female	total_male	purpose	main_activity	info_source	tour_arrangement	...	package_transport_tz	package_sightseeing	package_guided_tour	package_insurance	night_mainland	night_zanzibar	payment_mode	first_trip_tz	most_impressing	total_cost
0	tour_0	SWIZERLAND	45-64	Friends/Relatives	1.0	1.0	Leisure and Holidays	Wildlife tourism	Friends, relatives	Independent	...	No	No	No	No	13.0	0.0	Cash	No	Friendly People	674602.5
1	tour_10	UNITED KINGDOM	25-44	Alone	1.0	0.0	Leisure and Holidays	Cultural tourism	others	Independent	...	No	No	No	No	14.0	7.0	Cash	Yes	Wonderful Country, Landscape, Nature	3214906.5
2	tour_1000	UNITED KINGDOM	25-44	Alone	0.0	1.0	Visiting Friends and Relatives	Cultural tourism	Friends, relatives	Independent	...	No	No	No	No	1.0	31.0	Cash	No	Excellent Experience	3315000.0
3	tour_1002	UNITED KINGDOM	25-44	Spouse	1.0	1.0	Leisure and Holidays	Wildlife tourism	Travel, agent, tour operator	Package Tour	...	Yes	Yes	Yes	No	11.0	0.0	Cash	Yes	Friendly People	7790250.0
4	tour_1004	CHINA	1-24	Alone	1.0	0.0	Leisure and Holidays	Wildlife tourism	Travel, agent, tour operator	Independent	...	No	No	No	No	7.0	4.0	Cash	Yes	No comments	1657500.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
4804	tour_993	UAE	45-64	Alone	0.0	1.0	Business	Hunting tourism	Friends, relatives	Independent	...	No	No	No	No	2.0	0.0	Credit Card	No	No comments	3315000.0
4805	tour_994	UNITED STATES OF AMERICA	25-44	Spouse	1.0	1.0	Leisure and Holidays	Wildlife tourism	Travel, agent, tour operator	Package Tour	...	Yes	Yes	Yes	Yes	11.0	0.0	Cash	Yes	Friendly People	10690875.0
4806	tour_995	NETHERLANDS	1-24	Alone	1.0	0.0	Leisure and Holidays	Wildlife tourism	others	Independent	...	No	No	No	No	3.0	7.0	Cash	Yes	Good service	2246636.7
4807	tour_997	SOUTH AFRICA	25-44	Friends/Relatives	1.0	1.0	Business	Beach tourism	Travel, agent, tour operator	Independent	...	No	No	No	No	5.0	0.0	Credit Card	No	Friendly People	1160250.0
4808	tour_999	UNITED KINGDOM	25-44	Spouse	1.0	1.0	Leisure and Holidays	Wildlife tourism	Travel, agent, tour operator	Package Tour	...	Yes	Yes	Yes	No	4.0	7.0	Cash	Yes	Friendly People	13260000.0

4809 rows × 23 columns

#Most_impressing
Tz['most_impressing'].fillna('Friendly People',inplace=True)
Tz

	ID	country	age_group	travel_with	total_female	total_male	purpose	main_activity	info_source	tour_arrangement	...	package_transport_tz	package_sightseeing	package_guided_tour	package_insurance	night_mainland	night_zanzibar	payment_mode	first_trip_tz	most_impressing	total_cost
0	tour_0	SWIZERLAND	45-64	Friends/Relatives	1.0	1.0	Leisure and Holidays	Wildlife tourism	Friends, relatives	Independent	...	No	No	No	No	13.0	0.0	Cash	No	Friendly People	674602.5
1	tour_10	UNITED KINGDOM	25-44	Alone	1.0	0.0	Leisure and Holidays	Cultural tourism	others	Independent	...	No	No	No	No	14.0	7.0	Cash	Yes	Wonderful Country, Landscape, Nature	3214906.5
2	tour_1000	UNITED KINGDOM	25-44	Alone	0.0	1.0	Visiting Friends and Relatives	Cultural tourism	Friends, relatives	Independent	...	No	No	No	No	1.0	31.0	Cash	No	Excellent Experience	3315000.0
3	tour_1002	UNITED KINGDOM	25-44	Spouse	1.0	1.0	Leisure and Holidays	Wildlife tourism	Travel, agent, tour operator	Package Tour	...	Yes	Yes	Yes	No	11.0	0.0	Cash	Yes	Friendly People	7790250.0
4	tour_1004	CHINA	1-24	Alone	1.0	0.0	Leisure and Holidays	Wildlife tourism	Travel, agent, tour operator	Independent	...	No	No	No	No	7.0	4.0	Cash	Yes	No comments	1657500.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
4804	tour_993	UAE	45-64	Alone	0.0	1.0	Business	Hunting tourism	Friends, relatives	Independent	...	No	No	No	No	2.0	0.0	Credit Card	No	No comments	3315000.0
4805	tour_994	UNITED STATES OF AMERICA	25-44	Spouse	1.0	1.0	Leisure and Holidays	Wildlife tourism	Travel, agent, tour operator	Package Tour	...	Yes	Yes	Yes	Yes	11.0	0.0	Cash	Yes	Friendly People	10690875.0
4806	tour_995	NETHERLANDS	1-24	Alone	1.0	0.0	Leisure and Holidays	Wildlife tourism	others	Independent	...	No	No	No	No	3.0	7.0	Cash	Yes	Good service	2246636.7
4807	tour_997	SOUTH AFRICA	25-44	Friends/Relatives	1.0	1.0	Business	Beach tourism	Travel, agent, tour operator	Independent	...	No	No	No	No	5.0	0.0	Credit Card	No	Friendly People	1160250.0
4808	tour_999	UNITED KINGDOM	25-44	Spouse	1.0	1.0	Leisure and Holidays	Wildlife tourism	Travel, agent, tour operator	Package Tour	...	Yes	Yes	Yes	No	4.0	7.0	Cash	Yes	Friendly People	13260000.0

4809 rows × 23 columns

#total_male
Tz['total_male'].fillna(method='bfill',inplace=True)
Tz

	ID	country	age_group	travel_with	total_female	total_male	purpose	main_activity	info_source	tour_arrangement	...	package_transport_tz	package_sightseeing	package_guided_tour	package_insurance	night_mainland	night_zanzibar	payment_mode	first_trip_tz	most_impressing	total_cost
0	tour_0	SWIZERLAND	45-64	Friends/Relatives	1.0	1.0	Leisure and Holidays	Wildlife tourism	Friends, relatives	Independent	...	No	No	No	No	13.0	0.0	Cash	No	Friendly People	674602.5
1	tour_10	UNITED KINGDOM	25-44	Alone	1.0	0.0	Leisure and Holidays	Cultural tourism	others	Independent	...	No	No	No	No	14.0	7.0	Cash	Yes	Wonderful Country, Landscape, Nature	3214906.5
2	tour_1000	UNITED KINGDOM	25-44	Alone	0.0	1.0	Visiting Friends and Relatives	Cultural tourism	Friends, relatives	Independent	...	No	No	No	No	1.0	31.0	Cash	No	Excellent Experience	3315000.0
3	tour_1002	UNITED KINGDOM	25-44	Spouse	1.0	1.0	Leisure and Holidays	Wildlife tourism	Travel, agent, tour operator	Package Tour	...	Yes	Yes	Yes	No	11.0	0.0	Cash	Yes	Friendly People	7790250.0
4	tour_1004	CHINA	1-24	Alone	1.0	0.0	Leisure and Holidays	Wildlife tourism	Travel, agent, tour operator	Independent	...	No	No	No	No	7.0	4.0	Cash	Yes	No comments	1657500.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
4804	tour_993	UAE	45-64	Alone	0.0	1.0	Business	Hunting tourism	Friends, relatives	Independent	...	No	No	No	No	2.0	0.0	Credit Card	No	No comments	3315000.0
4805	tour_994	UNITED STATES OF AMERICA	25-44	Spouse	1.0	1.0	Leisure and Holidays	Wildlife tourism	Travel, agent, tour operator	Package Tour	...	Yes	Yes	Yes	Yes	11.0	0.0	Cash	Yes	Friendly People	10690875.0
4806	tour_995	NETHERLANDS	1-24	Alone	1.0	0.0	Leisure and Holidays	Wildlife tourism	others	Independent	...	No	No	No	No	3.0	7.0	Cash	Yes	Good service	2246636.7
4807	tour_997	SOUTH AFRICA	25-44	Friends/Relatives	1.0	1.0	Business	Beach tourism	Travel, agent, tour operator	Independent	...	No	No	No	No	5.0	0.0	Credit Card	No	Friendly People	1160250.0
4808	tour_999	UNITED KINGDOM	25-44	Spouse	1.0	1.0	Leisure and Holidays	Wildlife tourism	Travel, agent, tour operator	Package Tour	...	Yes	Yes	Yes	No	4.0	7.0	Cash	Yes	Friendly People	13260000.0

4809 rows × 23 columns

#total_female
Tz['total_female'].fillna(method='bfill',inplace=True)
Tz

	ID	country	age_group	travel_with	total_female	total_male	purpose	main_activity	info_source	tour_arrangement	...	package_transport_tz	package_sightseeing	package_guided_tour	package_insurance	night_mainland	night_zanzibar	payment_mode	first_trip_tz	most_impressing	total_cost
0	tour_0	SWIZERLAND	45-64	Friends/Relatives	1.0	1.0	Leisure and Holidays	Wildlife tourism	Friends, relatives	Independent	...	No	No	No	No	13.0	0.0	Cash	No	Friendly People	674602.5
1	tour_10	UNITED KINGDOM	25-44	Alone	1.0	0.0	Leisure and Holidays	Cultural tourism	others	Independent	...	No	No	No	No	14.0	7.0	Cash	Yes	Wonderful Country, Landscape, Nature	3214906.5
2	tour_1000	UNITED KINGDOM	25-44	Alone	0.0	1.0	Visiting Friends and Relatives	Cultural tourism	Friends, relatives	Independent	...	No	No	No	No	1.0	31.0	Cash	No	Excellent Experience	3315000.0
3	tour_1002	UNITED KINGDOM	25-44	Spouse	1.0	1.0	Leisure and Holidays	Wildlife tourism	Travel, agent, tour operator	Package Tour	...	Yes	Yes	Yes	No	11.0	0.0	Cash	Yes	Friendly People	7790250.0
4	tour_1004	CHINA	1-24	Alone	1.0	0.0	Leisure and Holidays	Wildlife tourism	Travel, agent, tour operator	Independent	...	No	No	No	No	7.0	4.0	Cash	Yes	No comments	1657500.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
4804	tour_993	UAE	45-64	Alone	0.0	1.0	Business	Hunting tourism	Friends, relatives	Independent	...	No	No	No	No	2.0	0.0	Credit Card	No	No comments	3315000.0
4805	tour_994	UNITED STATES OF AMERICA	25-44	Spouse	1.0	1.0	Leisure and Holidays	Wildlife tourism	Travel, agent, tour operator	Package Tour	...	Yes	Yes	Yes	Yes	11.0	0.0	Cash	Yes	Friendly People	10690875.0
4806	tour_995	NETHERLANDS	1-24	Alone	1.0	0.0	Leisure and Holidays	Wildlife tourism	others	Independent	...	No	No	No	No	3.0	7.0	Cash	Yes	Good service	2246636.7
4807	tour_997	SOUTH AFRICA	25-44	Friends/Relatives	1.0	1.0	Business	Beach tourism	Travel, agent, tour operator	Independent	...	No	No	No	No	5.0	0.0	Credit Card	No	Friendly People	1160250.0
4808	tour_999	UNITED KINGDOM	25-44	Spouse	1.0	1.0	Leisure and Holidays	Wildlife tourism	Travel, agent, tour operator	Package Tour	...	Yes	Yes	Yes	No	4.0	7.0	Cash	Yes	Friendly People	13260000.0

4809 rows × 23 columns

#Confirming if there still exist a null values.
Tz.isnull().sum()

ID                       0
country                  0
age_group                0
travel_with              0
total_female             0
total_male               0
purpose                  0
main_activity            0
info_source              0
tour_arrangement         0
package_transport_int    0
package_accomodation     0
package_food             0
package_transport_tz     0
package_sightseeing      0
package_guided_tour      0
package_insurance        0
night_mainland           0
night_zanzibar           0
payment_mode             0
first_trip_tz            0
most_impressing          0
total_cost               0
dtype: int64

sns.pairplot(Tz)

<seaborn.axisgrid.PairGrid at 0x2430913bf70>

Using the pairplotting above inorder to form some simple classification models by drawing some simple lines or make linear separation in our data-set to form a linear regression model.

Tz.plot.scatter(x='total_cost',y='night_mainland')

<AxesSubplot:xlabel='total_cost', ylabel='night_mainland'>

sns.lmplot(x='total_cost',y='night_mainland',data=Tz)

<seaborn.axisgrid.FacetGrid at 0x2430c0b7430>

#Chart showing the linear distribution of tourist spending.

import warnings
warnings.filterwarnings('ignore')

sns.distplot(Tz['total_cost']/10**6).set(title='SPENDING DISTIBUTION')

[Text(0.5, 1.0, 'SPENDING DISTIBUTION')]

#Data correlation of features.

Tz.corr()

	total_female	total_male	night_mainland	night_zanzibar	total_cost
total_female	1.000000	0.467000	0.031233	0.138523	0.285862
total_male	0.467000	1.000000	-0.041369	0.050172	0.183785
night_mainland	0.031233	-0.041369	1.000000	-0.118155	0.020473
night_zanzibar	0.138523	0.050172	-0.118155	1.000000	0.145139
total_cost	0.285862	0.183785	0.020473	0.145139	1.000000

sns.heatmap(Tz.corr(), annot=True)

<AxesSubplot:>

Generating a summary EDA checks on the dataset

#import pandas profiling library 

from pandas_profiling import ProfileReport
profile = ProfileReport(Tz)
profile

Summarize dataset:   0%|          | 0/5 [00:00<?, ?it/s]

Generate report structure:   0%|          | 0/1 [00:00<?, ?it/s]

Render HTML:   0%|          | 0/1 [00:00<?, ?it/s]

EXPLORATORY DATA ANALYSIS

#top 5 countries with the highest spending statistics

countries = Tz[['country', 'total_cost']].head(8).groupby('country').sum()
countries

	total_cost
country
CHINA	1657500.0
SOUTH AFRICA	466140.0
SWIZERLAND	674602.5
UNITED KINGDOM	14441106.5
UNITED STATES OF AMERICA	3480750.0

#CHAT SHOWING TOP 5 COUNTRIES WITH HIGHEST SPENDING STATISTICS

plt.style.use('seaborn')
countries.plot(figsize=(12,3), color='red', legend=False, title='TOP 5 COUNTRIES WITH HIGHEST SPENDING STATISTICS')

<AxesSubplot:title={'center':'TOP 5 COUNTRIES WITH HIGHEST SPENDING STATISTICS'}, xlabel='country'>

Of all the five countries shown above, United Kingdom indicates the leading top most country in tourist spending.

#COUNTRIES WITH HIGHEST TOURIST CITIZENS VISITING TANZANIA.

Tz[['country']].value_counts()

country                 
UNITED STATES OF AMERICA    695
UNITED KINGDOM              533
ITALY                       393
FRANCE                      280
ZIMBABWE                    274
                           ... 
ANGOLA                        1
MONTENEGRO                    1
MORROCO                       1
MYANMAR                       1
MADAGASCAR                    1
Length: 105, dtype: int64

Tz[['country']].value_counts().head(20).plot(kind='barh', title='COUNTRIES WITH HIGHEST TOURIST CITIZENS VISITING TANZANIA')

<AxesSubplot:title={'center':'COUNTRIES WITH HIGHEST TOURIST CITIZENS VISITING TANZANIA'}, ylabel='country'>

#which age-group are the highest spenders and who are the over all highest spenders by travel with

Age = Tz[['age_group','total_cost','travel_with']]
Age

	age_group	total_cost	travel_with
0	45-64	674602.5	Friends/Relatives
1	25-44	3214906.5	Alone
2	25-44	3315000.0	Alone
3	25-44	7790250.0	Spouse
4	1-24	1657500.0	Alone
...	...	...	...
4804	45-64	3315000.0	Alone
4805	25-44	10690875.0	Spouse
4806	1-24	2246636.7	Alone
4807	25-44	1160250.0	Friends/Relatives
4808	25-44	13260000.0	Spouse

4809 rows × 3 columns

Age.describe(include = 'all')

	age_group	total_cost	travel_with
count	4809	4.809000e+03	4809
unique	4	NaN	5
top	25-44	NaN	Alone
freq	2487	NaN	2379
mean	NaN	8.114389e+06	NaN
std	NaN	1.222490e+07	NaN
min	NaN	4.900000e+04	NaN
25%	NaN	8.121750e+05	NaN
50%	NaN	3.397875e+06	NaN
75%	NaN	9.945000e+06	NaN
max	NaN	9.953288e+07	NaN

Age.groupby('age_group').sum()/10**6

	total_cost
age_group
1-24	3379.088150
25-44	14987.099938
45-64	15371.839260
65+	5284.068284

#HIGHEST SPENDING AGE GROUP
Age.groupby('age_group').sum().plot(kind='bar',title='HIGHEST SPENDING AGE GROUP')

<AxesSubplot:title={'center':'HIGHEST SPENDING AGE GROUP'}, xlabel='age_group'>

Age group 45-64 has the highest spending statistics.

Age.groupby('travel_with').sum()

	total_cost
travel_with
Alone	8.717835e+09
Children	1.653502e+09
Friends/Relatives	9.158700e+09
Spouse	1.274631e+10
Spouse and Children	6.745753e+09

#Highest age_group Tourist.

Age[['age_group']].value_counts()

age_group
25-44        2487
45-64        1391
1-24          624
65+           307
dtype: int64

Age[['age_group']].value_counts().plot(kind='pie', autopct='%1.1f%%')

<AxesSubplot:ylabel='None'>

A pie chart show age_group representation in tourism activities in Tanzania

#COUNTRY WITH THE MOST SPENDING TOURIST

Tz[['country','total_cost']].head(10)

	country	total_cost
0	SWIZERLAND	674602.5
1	UNITED KINGDOM	3214906.5
2	UNITED KINGDOM	3315000.0
3	UNITED KINGDOM	7790250.0
4	CHINA	1657500.0
5	UNITED KINGDOM	120950.0
6	SOUTH AFRICA	466140.0
7	UNITED STATES OF AMERICA	3480750.0
8	NIGERIA	994500.0
9	INDIA	2486250.0

Tz[['country','total_cost']].head(10).plot(kind='bar',title='COUNTRY WITH THE MOST SPENDING TOURIST')

<AxesSubplot:title={'center':'COUNTRY WITH THE MOST SPENDING TOURIST'}>

United Kindom (3) is the country with the most spending tourist even though it falls second to USA with the highest number of tourist visiting Tanzania.

#AVERAGE NUMBER OF NIGHT TOURIST'S SPENDS ON TANZANIA MAINLAND

Tz[['night_mainland']].mean()

night_mainland    8.488043
dtype: float64

An average eight (8) number of nights is spend by a tourist on Tanzania mainland

#AVERAGE NUMBER OF NIGHT TOURIST'S SPENDS ON TANZANIA ZANZIBAR

Tz[['night_zanzibar']].mean()

night_zanzibar    2.304429
dtype: float64

Two(2) nights averagely is spent on Tanzania Zanzibar by tourist.

#MOST PREFERRED PAYMENT METHOD BY TOURIST

Tz[['payment_mode']].head(30)

	payment_mode
0	Cash
1	Cash
2	Cash
3	Cash
4	Cash
5	Cash
6	Cash
7	Cash
8	Cash
9	Credit Card
10	Cash
11	Cash
12	Cash
13	Cash
14	Cash
15	Cash
16	Cash
17	Cash
18	Cash
19	Cash
20	Cash
21	Cash
22	Credit Card
23	Cash
24	Cash
25	Cash
26	Credit Card
27	Cash
28	Cash
29	Cash

type(Tz['payment_mode'].iloc[0])

str

#CHART SHOWING MOST USED PAYMENT MODE BY TOURIST

plt.style.use('ggplot')
sns.catplot(data=Tz, x='payment_mode', kind='count').set(title='PAYMENT MODE USED')

<seaborn.axisgrid.FacetGrid at 0x2431a874790>

The visualization above indicate most tourist prefered payment via cash.

#TOURISM MAIN ACTIVITIES IN TANZANIA

Tz[['main_activity']].head(10)

	main_activity
0	Wildlife tourism
1	Cultural tourism
2	Cultural tourism
3	Wildlife tourism
4	Wildlife tourism
5	Wildlife tourism
6	Mountain climbing
7	Wildlife tourism
8	Cultural tourism
9	Wildlife tourism

sns.catplot(data=Tz, y='main_activity', kind='count')

<seaborn.axisgrid.FacetGrid at 0x2431aa3d760>

The tourist are more engaged in wildlife and beach tourism activities every time they visit with Wildlife more participated.

Tz[['package_food']]

	package_food
0	No
1	No
2	No
3	Yes
4	No
...	...
4804	No
4805	Yes
4806	No
4807	Yes
4808	Yes

4809 rows × 1 columns

sns.catplot(data=Tz, x='package_food', kind='count')

<seaborn.axisgrid.FacetGrid at 0x243195d83d0>

Observation above, indicates most tourist prefer No package food during there tourist activities, this could be it's more delicious and safer or flexible in eating.

DATA PROCESSING

#FEATURE ENGINEERING
#Getting arrays with features to train on

Tz.columns

Index(['ID', 'country', 'age_group', 'travel_with', 'total_female',
       'total_male', 'purpose', 'main_activity', 'info_source',
       'tour_arrangement', 'package_transport_int', 'package_accomodation',
       'package_food', 'package_transport_tz', 'package_sightseeing',
       'package_guided_tour', 'package_insurance', 'night_mainland',
       'night_zanzibar', 'payment_mode', 'first_trip_tz', 'most_impressing',
       'total_cost'],
      dtype='object')

#PREPARING DATA FOR MODELLING

#features for modelling
X = Tz[['total_female',
       'total_male','night_mainland','night_zanzibar'
       ]]

#Target variable or Prediction variable.

Y = Tz[['total_cost']]

#Train test split

from sklearn.model_selection import train_test_split

X_test,X_train, Y_test,Y_train = train_test_split(X,Y, test_size=0.9, random_state=60)

BUILDING A REGRESSION MODEL

#Creating and Training the model

from sklearn.linear_model import LinearRegression

#Instantiate model

lm = LinearRegression()

lm.fit(X_train,Y_train)

LinearRegression()

print(lm.intercept_)

[4462382.63431905]

lm.coef_

array([[2071453.64220804,  748866.97405083,   35552.52558308,
         293057.24098337]])

X_train.columns

Index(['total_female', 'total_male', 'night_mainland', 'night_zanzibar'], dtype='object')

EVALUATING OUR MODEL PERFORMANCE IN PREDICTING SPENDING BEHAVIOUR

PREDICTIONS

prediction = lm.predict(X_test)

#Predicted spending cost of tourist/ indicating their spending behaviours
prediction

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#Y_test containing the correct spending cost/habit of tourist
Y_test

	total_cost
4284	130000.0
3401	8453250.0
3204	7293000.0
3362	497250.0
3822	6132750.0
...	...
2147	6298500.0
1418	3480750.0
3654	8619000.0
3137	600000.0
2253	497250.0

480 rows × 1 columns

#Comparing Y_test to the prediction to find the residuals
plt.scatter(Y_test, prediction/10**6)

<matplotlib.collections.PathCollection at 0x2431ae2a1f0>

#Plotting histogram distribution of the residuals
sns.distplot(Y_test- prediction/10**6)

<AxesSubplot:ylabel='Density'>

EVALUATING THE MODEL With Loss functions so ass to minimise the errors.

REGRESSION EVALUATION METRICS:

#Looking at the Linear evaluation metrics we get:

from sklearn import metrics

#Average error of the model
metrics.mean_absolute_error(Y_test,prediction)

7232385.834148328

#Check-mating larger errors by squaring.

metrics.mean_squared_error(Y_test,prediction)

121440898468113.73

#RMSE Interpreting the Y units prediction.

np.sqrt(metrics.mean_squared_error(Y_test,prediction))

11020022.616497379

r_squared = lm.score(X, Y)

print(r_squared)

0.09682259179087571

r_squared = lm.score(X_test,prediction)

print(r_squared)

1.0

from sklearn.metrics import r2_score

r2_score(Y_test,prediction)

0.1787447445823661

r2_score(Y_test,prediction).dtype

dtype('float64')

Our model reads r2_score of 18% approx performance in prediction.

TESTING LINEAR MODEL ASSUMPTION

Using/importing another dataset to test our model performance,

from sklearn.svm import SVC

tz = pd.read_csv('Test .csv')
tz

	ID	country	age_group	travel_with	total_female	total_male	purpose	main_activity	info_source	tour_arrangement	...	package_food	package_transport_tz	package_sightseeing	package_guided_tour	package_insurance	night_mainland	night_zanzibar	payment_mode	first_trip_tz	most_impressing
0	tour_1	AUSTRALIA	45-64	Spouse	1.0	1.0	Leisure and Holidays	Wildlife tourism	Travel, agent, tour operator	Package Tour	...	Yes	Yes	Yes	Yes	Yes	10	3	Cash	Yes	Wildlife
1	tour_100	SOUTH AFRICA	25-44	Friends/Relatives	0.0	4.0	Business	Wildlife tourism	Tanzania Mission Abroad	Package Tour	...	No	No	No	No	No	13	0	Cash	No	Wonderful Country, Landscape, Nature
2	tour_1001	GERMANY	25-44	Friends/Relatives	3.0	0.0	Leisure and Holidays	Beach tourism	Friends, relatives	Independent	...	No	No	No	No	No	7	14	Cash	No	No comments
3	tour_1006	CANADA	24-Jan	Friends/Relatives	2.0	0.0	Leisure and Holidays	Cultural tourism	others	Independent	...	No	No	No	No	No	0	4	Cash	Yes	Friendly People
4	tour_1009	UNITED KINGDOM	45-64	Friends/Relatives	2.0	2.0	Leisure and Holidays	Wildlife tourism	Friends, relatives	Package Tour	...	Yes	Yes	No	No	No	10	0	Cash	Yes	Friendly People
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1596	tour_988	UNITED STATES OF AMERICA	25-44	NaN	0.0	1.0	Meetings and Conference	Mountain climbing	Newspaper, magazines,brochures	Independent	...	No	No	No	No	No	1	0	Cash	No	NaN
1597	tour_990	ITALY	45-64	Spouse and Children	3.0	1.0	Leisure and Holidays	Wildlife tourism	Friends, relatives	Package Tour	...	Yes	Yes	Yes	No	No	10	5	Other	Yes	Wildlife
1598	tour_992	FINLAND	25-44	Alone	0.0	1.0	Meetings and Conference	Mountain climbing	Friends, relatives	Independent	...	No	No	No	No	No	6	0	Cash	Yes	No comments
1599	tour_996	SOUTH AFRICA	24-Jan	Alone	0.0	1.0	Business	Beach tourism	Friends, relatives	Independent	...	No	No	No	No	No	4	0	Cash	Yes	Wildlife
1600	tour_998	SOUTH AFRICA	25-44	Spouse	1.0	1.0	Leisure and Holidays	Cultural tourism	Radio, TV, Web	Independent	...	No	No	No	No	No	9	5	Cash	Yes	Friendly People

1601 rows × 22 columns

tz.corr()

	total_female	total_male	night_mainland	night_zanzibar
total_female	1.000000	0.288933	0.015265	0.078020
total_male	0.288933	1.000000	-0.035880	0.020622
night_mainland	0.015265	-0.035880	1.000000	0.516262
night_zanzibar	0.078020	0.020622	0.516262	1.000000

sns.heatmap(tz.corr(), annot=True)

<AxesSubplot:>

lm.fit(X,Y)

LinearRegression()

Y_pred = lm.predict(X_test)

Y_pred

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       [ 6737768.92128537],
       [13192002.96197375],
       [ 9830793.20760089],
       [ 9691438.14972328],
       [ 5014561.54549237],
       [ 5390541.00320452],
       [28504021.7220076 ],
       [ 6398702.62116335],
       [ 6635229.06918205],
       [ 5783602.2499698 ],
       [ 5766422.50910083],
       [ 8292711.79864603],
       [ 6532689.21707874],
       [ 5219641.249699  ],
       [ 5185461.29899789],
       [ 9745253.56532971],
       [ 5014561.54549237],
       [ 9505993.91042197],
       [ 8617511.09582494],
       [ 5082921.44689458],
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       [ 6737768.92128537],
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       [ 6635229.06918205],
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       [ 7184743.53547292],
       [ 9386413.05887603],
       [ 9127477.14945249],
       [ 5082921.44689458],
       [ 7438449.72436059],
       [ 5288001.15110121],
       [10682420.02681314],
       [20207154.56800148],
       [ 5458900.90460673],
       [ 7370089.82295838],
       [ 5151281.34829679],
       [16279503.67230584],
       [ 9059117.24805027],
       [ 7267549.97085506],
       [ 5185461.29899789],
       [ 5082921.44689458],
       [ 8227125.89374334],
       [ 8990757.34664806],
       [ 8856672.79891683],
       [ 9027572.55242235],
       [ 4929119.85503704],
       [ 8583331.14512384],
       [ 9505993.91042197],
       [ 7301729.92155617],
       [ 7472629.67506169],
       [ 5048741.49619347],
       [ 6601049.11848095]])

#Evaluate accuracy
from sklearn.metrics import accuracy_score
accuracy = lm.score(X_test,Y_pred)

accuracy

1.0

Interestingly, after testing our model with another datset(Test .csv) looking at the data correlations, our model was able to predict(Y_pred) the spending rate of the tourist considering the data is missing the total_cost column or feature. Our model achieve accuracy of 1.0(100%) prediction of spending habit of tourist in the tz data i.e Test .csv file.

X_test

	total_female	total_male	night_mainland	night_zanzibar
4284	0.0	1.0	3.0	0.0
3401	0.0	1.0	11.0	0.0
3204	1.0	1.0	0.0	7.0
3362	0.0	1.0	14.0	0.0
3822	0.0	1.0	5.0	0.0
...	...	...	...	...
2147	1.0	1.0	0.0	7.0
1418	1.0	1.0	2.0	0.0
3654	1.0	1.0	7.0	0.0
3137	0.0	1.0	2.0	0.0
2253	1.0	0.0	3.0	0.0

480 rows × 4 columns

Y_pred.sum()

3773139286.330149

Actionable Insights and Recommendations

Besides observation listed in the markdown text, The need for the tourism sector to pay more attention to tourism activities not attracting tourist attention such as

1. Business tourism which can boost the industry spread.
2. The Conference tourism, a major publicity strategy. As its upgrade and optimization will further attract interesting and engaging event to the venue.
3. The wildlife have seen monumental activities in the area with aid of experienced and skillful management. It will be interesting to tap into their potential to encourage their colleagues in other tourism sector.

Secondly, its been observed that most tourist prefer payment via direct cash. This is risky considering the currency exchange rate of Tanzanian shillings to Europe, USA, etc. making tourist to move about with large sum of money. It will be preferable to encourage an electronic payment system which has more value return on investment. Lastly, non packaged food is mostly preferred by tourist due to its flexibility, sweetness and freshness. So a more priority to be channel towards its expansion.

NOTE: The model chosen for this project didn't really fit into the dataset given, producing a not too regular regression pattern which may affect the model durability & performance in future. Using other regression model such as Three distribution, etc will be really helpful in future development.